Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version
We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints. In the scheme, we explore the idea of using tracking MPC with artificial references to let agents coordinate their cooperation without external guidance. Each agent combines a tracking MPC with artificial references, the latter penalized by a suitable coupling cost. They solve an individual optimization problem for this artificial reference and an input that tracks it, only communicating the former to its neighbors in a communication graph. This puts the cooperative problem on a different layer than the handling of the dynamics and constraints, loosening the connection between the two. We provide sufficient conditions on the formulation of the cooperative problem and the coupling cost for the closed-loop system to asymptotically achieve it. Since the dynamics and the cooperative problem are only loosely connected, classical results from distributed optimization can be used to this end. We illustrate the scheme's application to consensus and formation control.
Code (0)
등록된 구현이 없습니다.
Tasks
Distributed OptimizationModel Predictive ControlSimilar Papers 제목 키워드 기반
Local Wealth Redistribution Promotes Cooperation in Multiagent Systems
Designing mechanisms that leverage cooperation between agents has been a long-lasting goal in Multiagent Systems. The task is especially challenging when agents are selfish, lack common goals and face social dilemmas, i.…
Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems
In autonomic computing, self-adaptation has been proposed as a fundamental paradigm to manage the complexity of multiagent systems (MASs). This achieved by extending a system with support to monitor and adapt itself to a…
Language ModelingLanguage ModellingLarge Language ModelA Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning
Multiagent reinforcement learning (MARL) can solve complex cooperative tasks. However, the efficiency of existing MARL methods relies heavily on well-defined reward functions. Multiagent tasks with sparse reward feedback…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Heterogeneous Agent Cooperation via Multiagent League Training
Many multiagent systems in the real world include multiple types of agents with different abilities and functionality. Such heterogeneous multiagent systems have significant practical advantages. However, they also come …
Diversityreinforcement-learningReinforcement LearningReinforcement Learning (RL)Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
Large-scale heterogeneous multiagent systems feature various realistic factors in the real world, such as agents with diverse abilities and overall system cost. In comparison to homogeneous systems, heterogeneous systems…
reinforcement-learningReinforcement Learning